Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly.

ABSTRACT: While polymerization-induced self-assembly (PISA) has become a preferred synthetic route toward amphiphilic block copolymer self-assemblies, predicting their phase behavior from experimental design is extremely challenging, requiring time and work-intensive creation of empirical phase diagrams whenever self-assemblies of novel monomer pairs are sought for specific applications. To alleviate this burden, we develop here the first framework for a data-driven methodology for the probabilistic modeling of PISA morphologies based on a selection and suitable adaption of statistical machine learning methods. As the complexity of PISA precludes generating large volumes of training data with in silico simulations, we focus on interpretable low variance methods that can be interrogated for co

SUBMITTER: Lu Y 

PROVIDER: S-EPMC10268968 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature